Summary of Mit-10m: a Large Scale Parallel Corpus Of Multilingual Image Translation, by Bo Li et al.
MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation
by Bo Li, Shaolin Zhu, Lijie Wen
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a large-scale parallel corpus called MIT-10M for image translation, which enables the translation of textual content within images into various languages. The dataset contains over 10 million image-text pairs derived from real-world data and has undergone extensive cleaning and multilingual translation validation. It features 840K images in three sizes, 28 categories, and tasks with three levels of difficulty across 14 languages. The authors evaluate and train models on MIT-10M, demonstrating its adaptability and superiority over existing datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a huge database that helps computers understand what’s written in pictures from different countries and languages. This is important because it can help us translate words from one language to another. The new database has lots of pictures and words, making it much better than the old ones. It also shows how well some computer programs do at translating these pictures. |
Keywords
» Artificial intelligence » Translation
